Technical Field
The present invention generally relates to computer vision and more particularly to object pose estimation.
Description of the Related Art
Object pose estimation is the key to solve many fundamental problems in computer vision, such as object recognition, human tracking, facial image analysis, etc. The pose estimation problem covers a wide range of types, i.e., human body pose estimation, head pose estimation, etc. It has drawn the attention of researchers, which have developed numerous methods. Due to the non-linearity of the pose variation and the specificity of each pose type, the problem is still extensively under investigation. Yet there are seldom methods that can handle more than one type of pose estimation problem.
Two of the mostly developed pose estimation problems are facial landmark localization and human body pose estimation. Head pose estimation is considered near-rigid because it is determined by the holistic movement of the head. However, when required to localize more finely defined key features, e.g., key positions of eye corners and mouth, the problem becomes non-linear because the key positions' movement relies on not only the head movement but also the local deformation caused by the non-linear facial skin and expressions. Human body pose estimation is a typical non-linear deformation problem because the body parts are articulated from each other. The movement of a part is rigid. But, when parts are connected as a holistic shape, the body movement is highly nonlinear because each part's movement is not consistent with others and the articulation causes folding of the parts.